gustavokpc/IC_segundo

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0559
• Train Accuracy: 0.9805
• Train F1 M: 0.5583
• Train Precision M: 0.4028
• Train Recall M: 0.9686
• Validation Loss: 0.2533
• Validation Accuracy: 0.9327
• Validation F1 M: 0.5605
• Validation Precision M: 0.4028
• Validation Recall M: 0.9674
• Epoch: 4

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3790, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Train F1 M	Train Precision M	Train Recall M	Validation Loss	Validation Accuracy	Validation F1 M	Validation Precision M	Validation Recall M	Epoch
0.3576	0.8399	0.4604	0.3607	0.7042	0.2825	0.8997	0.5635	0.4127	0.9300	0
0.2012	0.9274	0.5204	0.3849	0.8616	0.2103	0.9175	0.5451	0.3970	0.9095	1
0.1312	0.9511	0.5451	0.3969	0.9273	0.2125	0.9307	0.5571	0.4017	0.9523	2
0.0871	0.9690	0.5547	0.4007	0.9557	0.2417	0.9301	0.5565	0.4013	0.9547	3
0.0559	0.9805	0.5583	0.4028	0.9686	0.2533	0.9327	0.5605	0.4028	0.9674	4

Framework versions

• Transformers 4.34.1
• TensorFlow 2.14.0
• Datasets 2.14.5
• Tokenizers 0.14.1